Biology subjects

Minxha, J.

Publications and source records attributed to Minxha, J..

4 recordsLinked to original sources

bioRxiv2023

Abstract representations emerge in human hippocampal neurons during inference behavior

Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization1. However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning, and how they relate to behavior2,3. Here we characterized the representational geometry of populations of neurons (single-units) recorded in the hippocampus, amygdala, medial frontal cortex, and ventral temporal cortex of neurosurgical patients who are performing an inferential reasoning task. We find that only the neural representations formed in the hippocampus simultaneously encode multiple task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consisted of disentangled directly observable and discovered latent task variables. Interestingly, learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behavior suggests that abstract/disentangled representational geometries are important for complex cognition.

neuroscience↗

bioRxiv2020

Inferring brain-wide interactions using data-constrained recurrent neural network models

Behavior arises from the coordinated activity of numerous anatomically and functionally distinct brain regions. Modern experimental tools allow unprecedented access to large neural populations spanning many interacting regions brain-wide. Yet, understanding such large-scale datasets necessitates both scalable computational models to extract meaningful features of inter-region communication and principled theories to interpret those features. Here, we introduce Current-Based Decomposition (CURBD), an approach for inferring brain-wide interactions using data-constrained recurrent neural network models that directly reproduce experimentally-obtained neural data. CURBD leverages the functional interactions inferred by such models to reveal directional currents between multiple brain regions. We first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. We then apply CURBD to multi-region neural recordings obtained from mice during running, macaques during Pavlovian conditioning, and humans during memory retrieval to demonstrate the widespread applicability of CURBD to untangle brain-wide interactions underlying behavior from a variety of neural datasets.

neuroscience↗

bioRxiv2019

Value-related neuronal responses in the human amygdala during observational learning

AbstractThe amygdala plays an important role in many aspects of social-cognition and reward-learning. Here we aimed to determine whether human amygdala neurons are involved in the computations necessary to implement learning through observation. We performed single-neuron recordings from the amygdalae of human neurosurgical patients (male and female) while they learned about the value of stimuli through observing the outcomes experienced by another agent interacting with those stimuli. We used a detailed computational modeling approach to describe patients behavior in the task. Then, using both population-level decoding and single neuron analyses we found evidence to implicate amygdala neurons in two key computations relevant for observational-learning: tracking the expected future reward associated with a given stimulus, and in tracking outcome values received by oneself or other agents. Encoding and decoding analyses suggested observational value coding in amygdala neurons occurred in a different subset of neurons than experiential value coding. Collectively, these findings support a key role for the human amygdala in the computations underlying the capacity for learning through observation. Significance statementSingle neuron studies of the human brain provide a unique window into the computational mechanisms of cognition. In this study, epilepsy patients implanted intracranially with depth microelectrodes performed an observational learning task. We measured activity bilaterally in the amygdala and found a representation for observational rewards as well as observational expected reward values. Additionally, the representation of self-experienced and observational values was performed by distinct subsets of amygdala neurons. This study thus provides a rare glimpse into the role of human amygdala neurons in social cognition.

neuroscience↗

bioRxiv2019

Flexible recruitment of memory-based choice representations by human medial-frontal cortex

Decisions in complex environments rely on flexibly utilizing past experience as required by context and instructions1. This process depends on the medial frontal cortex (MFC) and the medial temporal lobe (MTL)2-5, but it remains unknown how these structures jointly implement flexible memory retrieval6,7. We recorded single neurons in MFC and MTL while human subjects switched8 between making memory- and categorization-based decisions9,10. Here we show that MFC rapidly implements changing task demands by utilizing different subspaces of neural activity during different types of decisions. In contrast, no effect of task demands was seen in the MTL. Choices requiring memory retrieval selectively engaged phase-locking of MFC neurons to field potentials in the theta-frequency band in the MTL. Choice-selective neurons in MFC signaled abstract yes-no decisions independent of behavioral response modality (button press or saccade). These findings reveal a novel mechanism for flexibly and selectively engaging memory retrieval11-14 and show that unlike perceptual decision-making15, memory-related information is only represented in frontal cortex when choices require it.

neuroscience↗